The present invention relates generally to tissue classification, and more particularly to classifying a pathological state of tissue based on image data.
Accurate detection of tissue metastases is essential in optimizing treatment for solid cancers. In particular, there is a need for non-invasive techniques for identifying such metastases. The following description will generally focus on detection of lymph-node metastases, but it should be understood that the present invention is not so limited, and can be applied to any type of tissue.
Magnetic resonance imaging (MRI) is a non-invasive technique for providing images with excellent anatomical detail and soft tissue contrast, but is generally unsuitable for detection of lymph-node metastases. However, the quality of MRI can be improved by using different imaging agents and acquisition techniques. More particularly, the quality of MRI can be improved by the use of contrast agents, such as intravenous administration of lymphotropic superparamagnetic nanoparticles. Such techniques are described in M. G. Harisinghani, J. Barentsz, P. F. Hahn, W. M. Desemo, S. Tabatabaci, C. H. van de Kaa, J. de la Rosette, and R. Weissleder, “Noninvasive Detection Of Clinically Occult Lymph-Node Metastases In Prostate Cancer,” N Engl J Med, vol. 348, no. 25, pp. 2491-2499, 2003; T. Shen, R. Weissleder, M. Papisov, A. Jr. Bogdanov, T J. Brady, “Monocrystalline Iron Oxide Nanocompounds (Mion):Physicochemical Properties,” Magn Reson Med., vol. 29, no. 5, pp. 599-604, 1993; and M. Harisinghani and R. Weissleder, “Sensitive Noninvasive Detection Of Lymph Node Metastases,” PloS Med 1(3), p. e66, 2004.
High quality MRI images, obtained using the techniques described above, may be used to detect lymph-node metastases using the following sequence of steps.                1. Detection        2. Segmentation        3. Classification; and        4. VisualizationDetection includes finding the location of a lymph node in the MRI images, and may be performed manually or using an automated algorithm. Detection is further described in M. Harisinghani and R. Weissleder, “Sensitive Noninvasive Detection of Lymph Node Metastases,” PloS Med 1(3), p. e66, 2004). Segmentation includes separating the lymph node from the surrounding area in the image, and may also be performed manually or using an automated algorithm. Segmentation is further described in G. Unal, G. Slabaugh, A. Yezzi and J. Tyan, “Joint Segmentation And Non-Rigid Registration Without Shape Priors”, SCR-04-TR-7495, Siemens Corporate Research (2004). In the detection and segmentation steps, several parameters are extracted from the image describing magnetic, geometric and spatial properties of the lymph-node tissue. These parameters, which will be discussed in further detail below, are used in the classification step to classify the pathological state of the lymph-node using a previously generated decision model. Finally, in the visualization step, the extracted and classified data of the lymph node are visualized, both in 2-dimensions and 3-dimensions, to allow for comprehensive description and support for a diagnosis. Further, the visualization may serve as a map to locate a malignant node during surgery.        
The classification step is generally performed using machine learning techniques which operate under supervision provided by a known classification for each of a number of training examples. The goal of machine learning is to discover any structure in the data that is informative for classifying the data. Statistical classification is a type of machine learning that takes a feature representation of data (e.g., image parameters) and maps it to a classification label. A classification algorithm is designed to learn (to approximate the behavior of) a function that maps a vector of parameters (X1, X2, . . . XN) into one of several classes by looking at several input-output examples (training data) of the function. The training data consists of pairs of input objects (vectors) from clinical data, and the pathological proven class. The output of the function can be a continuous value or can predict a class label of the input object.
The direct approach for classification is a linear analysis of the parameters, for example as described in M. Harisinghani and R. Weissleder, “Sensitive Noninvasive Detection Of Lymph Node Metastases,” PloS Med 1(3), p. e66, 2004. The aim is to find thresholds or cut-off values for the parameters, which discriminate best between the given classes based on a comparison with the proven class. A variety of methods exist for estimating these thresholds. However, the main drawback of this simple linear analysis is the fact that parameters can only be examined one at a time. We have found that no single parameter, by itself, has enough discriminatory power to accurately distinguish between benign and malignant lymph-nodes.